Abstract:
The paper
discusses the prospects of building educational systems which
integrate the
capabilities of hypermedia, learning environments, and
intelligent tutoring
systems. We present the ISIS-Tutor system, which
provides an example of
integrating hypermedia technology into an
intelligent learning environment.
ISIS-Tutor is intended for learning the
print formatting language of an
information retrieval system, CDS/ISIS.

Introduction

Many developers of
educational systems consider Intelligent Tutoring
Systems (ITS) and
learning environments as different and even
contradictory ways of using
computers in education. The recent success of
such well-known Intelligent
Learning Environments (ILE) Smithtown (Shute &
Glaser, 1990) and Sherlock
(Lajoie & Lesgold, 1989) showed that these ways
are not contradictory, but
rather complimentary. ITSs are able to control
learning adaptively on
various levels, but generally do not provide tools
to support free
exploration. Learning environments and microworlds support
exploratory
learning, but they lack the control of an intelligent tutor.
Without such
control the student often works inefficiently and may never
discover
important features of the subject.

The same situation exists now
with ITSs and educational hypermedia
systems. They are often considered as
two different approaches to using
computers in education, but these
approaches are in fact complimentary.
Recent research has demonstrated that
hypermedia can provide the basis for
an exploratory learning system but
that, by itself, such a system is
insufficient, needing to be supplemented
by more directed guidance
(Hammond, 1989). The guidance can be provided by
an intelligent tutoring
component. By comparison, hypermedia can add new
dimensions to traditional
ITS/ILE by providing a tool for student-driven
acquisition of domain
knowledge.

We think that in many domains it is
possible to achieve a good result by
developing an educational computer
system which integrates the
capabilities of an intelligent tutor, a
learning environment, and a
hypermedia system. Such an integrated system
can support the learning of
both procedural, and declarative knowledge and
provide both
system-controlled and student-controlled styles of learning
(Tab.1). The
latteris essentially important because recent studies
reported that
optimal balance between student-controlled and
system-controlled
strategies depends on the student's abilities and
knowledge level. Only an
integrated system can support the whole continuum
of learning styles
ranging from an unstructured instructional environment
to a guided curriculum.

Table 1. Knowledge, styles of
learning and supporting systems

Procedural knowledge

Declarative
knowledge

Teaching

ITS (coach)

ITS
(tutor)

Learning

Learning
environments

Hypermedia systems

Our research at the Moscow State University are
centered around two
problems of creating integrated ILE: the problem of
adaptation and the
problem of integration.
As for adaptation, the
problem is to make all the components of an
integrated ILE adaptive. Most
of ITS and tutoring components of ILE can
adapt its work (tutoring) to the
given student, however very few
environment and manual components can do
that. It was one of our goals to
build adaptive environment and manual
components of ILE. As for
integration, our position is that an
integrated system should be not
just a sum but a real integration of its
components. In particular, it
requires the continuity of student
work in an integrated ILE. The
results of students' work with any of the
components during the session
should be taken into account by other
components to adapt their
performance to the changed knowledge level and
current interest of the
particular student.

As a solution for the
above two problems we designed a knowledge based
approach and a simple
student model centered architecture for building
integrated ILE. By this
approach all the components of ILE including the
environment use the same
central knowledge base (which include central
student model, a traditional
part of ITS) to coordinate its behavior and
to adapt it to the given
student. Specific structure of the knowledge
representation and and
specific techniques for work with it forms the
specifics of the approach.
Since 1985 we applied the approach and the
student model centered
architecture in several ILE designed by our group
for different domains. We
use these ILE to investigate various aspects and
problems of integrated
ILE. During the last few years we extended our
approach by integrating
hypermedia technology into our existing ILEs.

This paper describes
briefly our knowledge based approach to building a
hypermedia-based
intelligent learning environment (Brusilovsky, Pesin &
Zyryanov, 1993) and
then presents the ISIS-Tutor system, which is an
intelligent learning
environment intended for learning the print
formatting language of an
information retrieval system CDS/ISIS/M. From
our point of view, ISIS-Tutor
provides an interesting example of
integrating a hypermedia technology into
an intelligent learning
environment. Separately we discuss some interesting
features of
ISIS-Tutor: the use of student model centered architecture and
adaptive
navigation support.

Integrating a hypermedia component into ILE:
the knowledge based approach

Our approach to building integrated ILE
is based on the ideas from the
domains of intelligent tutoring systems and
adaptive interfaces. This
approach was first implemented in 1985 in the
ITEM/IP system (Brusilovsky
1992b) which integrates an ITS for programming,
a programming environment,
and adaptive on-line help facilities on the
basis of represented knowledge
about the domain and the student. The
following kinds of knowledge are
represented. The domain model in ITEM/IP
is a net with nodes corresponding
to programming concepts and mini-language
constructs (domain knowledge
elements), and with links reflecting several
kinds of relations between
nodes. The overlay student model reflects by a
set of integer counters the
extent to which the student has mastered the
concept or the construct. The
student model is always kept up-to-date and
supports adaptive work of all
modules. All the teaching material is stored
in the knowledge base of
teaching operations in the form of frames
interlinked with the domain
model. These frames are used to generate
several kinds of teaching
operations (presentation, example, problem, etc).

ITEM/IP uses the represented knowledge to support adaptive guidance
and
adaptive presentation. At any moment of work the student can ask
the
system for the next "best" teaching operation. The strategy module
applies
an embedded tutoring strategy and uses the domain knowledge and
the
student model to select such a teaching operation. A student who is
not
satisfied with the optimal operation suggested by the system, can
choose
any relevant teaching operation from menus. The presentation module
uses
the represented knowlege about the domain and the student to
provide
adaptive presentation of the selected teaching operation.
Briefly
speaking, the presentation of teaching material is adapted to the
current
level of student knowledge, so a repeated explanation is usually
more
concise and complete than the explanation presented when a concept
is
initially introduced.

The ITEM/IP approach was later generalised
and applied sucessfully in a
number of other domains. However we were not
completely satisfied with the
menu-based interface for student-driven
learning. In our most recent works
we have been trying to extend our
approach by integrating the ITS and
hypermedia technologies. The problem
here is not just to design a
hypermedia component to support student-driven
learning for one of our
existing ILEs, but to find the ways to integrate
this component into a
particular ILE. It means that the hypermedia
component must both use the
student model to adapt its performance to the
given student, and update
the student model to reflect the results of the
student's work with the
component.

We found that the knowledge based
approach is very suitable for
integration of intelligent tutoring and
hypermedia technologies. We need
only "two steps" to make a hypermedia
network really integrated in our
ILE. The first step is to take the domain
model network of an ILE as a
basis for the hypermedia network. Actually,
different ideas about creating
the hypermedia network from the domain
concept network are quite popular
and have been discussed elsewhere
(Hendley, Whittington & Jurascheck,
1993). What we suggest is to design the
main part of the hypermedia
network just as the visualized (and
externalized) domain network. Each
node of the domain network should be
represented by a node of the
hyperspace, while the links between domain
network nodes constitute main
paths between hyperspace nodes. Thus the core
structure of the overall
hyperspace resembles the pedagogic structure of
the domain knowledge. In
addition to that, each teaching operation is also
represented as a node of
the hyperspace and interlinked with all domain
concepts listed in its
spectrum. It provides natural interface for
student-driven learning.

The second step concerns the content of
hypermedia pages. We decided not
to duplicate the learning material in the
form of static hypermedia pages,
but to have the hypermedia pages generated
from the material stored in the
knowledge base of learning material. Thus,
each concept and each teaching
operation will have a hypermedia page (or
block of pages) as an external
representation, and have a frame as an
internal representation. What the
student will see on the screen visiting a
hypermedia node is really
generated from the corresponding frame by a
special program which can take
into account the student's current state of
knowledge. This approach not
only saves page design time but also provides
space for adaptation.

The knowledge-based approach enables
bi-directional communication between
the heart of the system - the student
model and the hypermedia component.
First, any student's 'visits' to a
particular node of the network can be
reflected in the corresponding compone
nt of the overlay student model
(which is based on the same domain
network). Second, the current state of
the student model for the given node
of the network and its links can be
used by the hypermedia component to
adapt the screen layout of the
corresponding hyper-node, as well as the
number and status of visible
links to other nodes. More considerations
about integration of hypermedia
into an intelligent learning environment,
including a review of related
works, can be found in (Brusilovsky, Pesin &
Zyryanov 1993; Brusilovsky,
1994).

The following section explains our
way of building hypermedia-based ILE on
the example of ISIS-Tutor system.

ISIS-Tutor: The domain and the
architecture

ISIS-Tutor is an intelligent learning environment to
support learning the
print formatting language of the well known
information retrieval system
CDS/ISIS/M for IBM PCs (ISIS for short). This
system is supplied by UNESCO
and used widely in Russia and in many
information centres in the world.
The print formatting language is key to
many CDS/ISIS operations and
mastering the language is important for
effective use of the system. In
some ways, it is a kind of programming
language. To display or print the
result of a search, or the content of a
database, an ISIS user has to
write a sequence of print formatting
commands, really, a more-or-less
complex program in print formatting
language. This format program is used
by ISIS produce an external
presentation of the record when displaying or
printing it.
To print the
selected records of a database in the
specified format ISIS applies the
print formatting program to every record
being printed. Print formatting
commands, for example, can type a field of
the current record or a part of
field, can manipulate the current output
position, type a constant
character string, and so on. Print formats are
also used in indexing and
sorting. There are over 50 different commands
and modifiers in the print
formatting language, so a tutoring system for
the language is really
helpful.

The ISIS-Tutor system is designed in International Centre
for Scientific
and Technical Information (ICSTI) and Moscow State
University. ISIS-Tutor
is written in embedded ISIS-Pascal and uses the
power of ISIS for
knowledge bases storage and access. Earlier versions of
ISIS-Tutor was
described in (Pesin & Brusilovsky, 1992; Brusilovsky &
Pesin, 1994) In
this paper we present the most recent version of ISIS-Tutor
which provides
an example of integrating hypermedia technology into an
intelligent
learning environment (Figure
1).

Figure 1. Top-level menu of
ISIS-Tutor

ISIS-Tutor architecture resembles the
original architecture of ITEM/IP
(Brusilovsky 1992b) in many ways: it also
contains an environment to
experiment with the language, and a tutor
component. It uses similar
domain and student models. However the domain
model, which is a network of
69 concepts and constructs, is twice as
complex than this one in ITEM/IP.
The overall 'space' of teaching material
is bigger as well. Unlike
ITEM/IP, ISIS-Tutor contains the hypermedia
component. Main components of
ISIS-Tutor are the following:

The interrelated domain model and student model form
the heart of
the system. It makes the system integrated and adaptive.
Modules of
ISIS-Tutor use the student model to adapt their work and update
it to
reflect the student's progress.

The tutor component
supports adaptive task sequencing
(Brusilovsky, 1992a), which means that
knowledge demonstrated by the
student in the past is analyzed and the
system selects an optimal teaching
operation to present. The component
deals with three kinds of teaching
operations: concept presentations,
examples and problems. Using the
Student Model and task spectra the tutor
can select an optimal teaching
operation for the given student in each
stage of learning.

The hypertext component supports
student-driven acquisition of
conceptual knowledge. It is an integrated
part of the system. It means
that the component uses the student
model to provide adaptive
navigation support for the given student, and it
updates the
student model to reflect the results of the student's
work with the
component.

The learning environment allows the
user to play and experiment
with print formatting commands. It provides
step-by-step execution and
extended visualization. Student work in this
component is also reflected
in the student model.

The
integration of guided tutoring mode (provided by the tutor) with
free
exploration mode (provided by the hypermedia and the environment) is
the
most interesting feature of ISIS-Tutor. The following section
describes
ISIS-Tutor components in more details.

ISIS-Tutor components

The central
part of ISIS-Tutor architecture is the interrelated domain
model and
student model. The domain model representes the material
being taught
(knowledge about language). The material is divided into a
set of
elementary concepts, which represents various features of the
language, and
structured as a directed graph (concept map) representing
prerequisite
relationships between concepts. The overlay student model
(SM) reflects the
extent to which the student has mastered language
concepts by providing an
integer counter for each concept. All learning
material is also interlinked
with the domain model network. There are
three kinds of teaching operations
in ISIS-Tutor: concept presentations,
problems to solve and examples to
analyze. The teaching operations (or
learning tasks from student's point of
view) are stored in the knowledge
base of learning material. Each teaching
operation is represented as
frame. One of its slots contains the list of
domain concepts related with
the task; for example, the list of concepts
required to solve the problem.
This list (called the spectrum) provides the
link with the domain model.

Each step of the learning process in
ISIS-Tutor is an application of one
of two main teaching operations:
selection and solving of a problem, or
selection and presentation of a new
concept. The choice can be made by the
system or by the user. In the first
case, the system uses the student
model to select the optimal teaching
operation. This mode gives an
adaptive sequence of tasks and concepts. In
the second case the user
selects the next teaching operation using the
hypermedia component. This
mode can be useful for an experienced student or
for a teacher, who wants
to set a learning task manually for the student
(Figure 1 and Figure 2). Important
is that the
student can always request a repeated presentation of a learned
concept or
a solved problem. This feature is very useful, essentially for a
weak
student, who has not mastered the material very well.

In any case the selected teaching
operation is a concept presentation (Figure 3 and Figure 4) or a
problem solution (Figure
5). The example presentation is at present not an
independent teaching
operation (as it was in ITEM IP). It follows a
concept presentation and can
be requested from the concept presentation
window (Figure
3).

Figure 3. Topic presentation page
for topic 8 "Field Output". A menu of related examples is shown
in the
center. Each example is a link to the exploratory environment (see Figure 6).

The learning process is
managed by the intelligent tutor. Using SM
and an embedded
strategy, the tutor can select an optimal teaching
operation - new concept
presentation or problem, depending on the current
state of user knowledge.
The tutor usually tries to offer a problem for
mastering a concept which is
known but not mastered enough by solving
problems. If there are no relevant
problems, then a new concept for study
is selected. After a teaching
operation is completed the tutor updates SM.
Initially SM is empty. While
the user works with the system the model is
updated in parallel with the
growth of the user's knowledge.

Figure 4. Topic presentation page for topic 5 "New Line". A
menu of related topics is shown
in the center. Each menu line is a link to
the presentation of this topic.
Link annotation with different colors is
used for adaptive navigation support. Hiding is disabled.
Bottom line -
annotation keys (same as on Figure 2):
+(green) Well
learned, (green) Learned, (red) Ready to be learned, (no annotation) Not
ready

The hypermedia module supports
student-driven access to the
teaching material. The teaching material in
ISIS-Tutor forms a hypermedia
network. Presentation of any concept or
construct includes providing
generated lists of related concepts, examples
and problems (Figure 3 and Figure
4). Important is
that all the above lists includes both known and new
concepts and
examples, as well as solved and not solved problems (as we
will see later,
to distinguish known concepts from new they are marked in
two different
colours). The student can select any related concept or
example from the
generated list to move to a related page of teaching
material (Figure 4). The
selected concept, example, or
problem is presented to the student, who can
read information about the
concept, experiment with an example, or try to
solve the problem (Figure 5). When selecting an example (usually a program
fragment
using the original concept) the student moves to the
programming
environment component with the selected example loaded for
interpretation.
The student can play with the example using the visualizing
interpreter,
changing the data and the program example itself. When solving
a problem
the student also can use the programming environment.
Presentation of both
examples and problems includes "reverse" links: from a
problem or an
example to all concepts from the spectrum of these teaching
operations.

Using the described
approach we build a naturally structured and tightly
interlinked hyperspace
of educational material, which supports advanced
navigation. For example,
the user can start from a domain concept, then
move to a related construct,
then to some example of it's application.
Here the user can enter the
environment to play with the example, then
move back to the construct and
repeat it with another example. Then the
user can select one of the
problems related with the construct to master
the obtained knowledge. If
the problem appears to be hard, the student can
analyse the list of
concepts in the problem spectrum and move from a
problem to the concept
which is not clear yet (and which can be far away
in the network from the
the starting concept. Thus the user in ISIS-Tutor
has many ways of
navigation and many paths going from the current node to
related nodes.

To help the user to navigate in this tightly interlinked hyperspace
the
hypermedia component applies an adaptive navigation support technique.
The
idea of adaptive navigation support in ISIS-Tutor is to annotate the
set
of links leading from the current node to related nodes (and from
index
page to all nodes) according to the current user knowledge and
educational
goals. The student model and the hypermedia component
distinguish four
knowledge states for each concept and related
hypernode:
not-ready-to-be-learned (i.e. has unlearned
prerequisites),
ready-to-be-learned, in-work(learning started), and learned
(user
demonstrate that he knows the concept by solving the required number
of
problems). Thus, at any moment the hyperspace is divided implicitly
into
four zones with different educational status.
Our idea is that
different zones have different meanings for the student
and marking these
zones visually would help the student in hyperspace
navigation. To mark the
zones the hypermedia component just marks the
hyper-links of each node
using different colours and some special
characters. For example, the links
to the nodes which are
not-ready-to-be-learned can be dimmed so as not to
distract the student.
Similar technique is used to adapt the link
presentation to current
educational goal.

The educational
goal in ISIS-Tutor is just the set of concepts
which the student
expected to learn at the current session. The goal can
be prescribed by the
human teacher of by the student himself. We use two
kinds of goal
adaptation: hiding all the concepts outside the current goal
(which really
restricts the student to this goal) and outlining the goal
concepts. Thus,
the links from the index or current pages to related
concept pages have can
have different colours and special marks attached
to it, and that tells the
student about the educational and goal states of
the related pages (Figure 2). Similar ways are used the represent
different
educational states of related problems and examples (Figure 4).

Using colours to support adaptive
navigation is definitely not the best
way. Generally, colours are
meaningless for the student. In addition, too
many different colours on the
screen is very distractive. We think that
using icon-based (de La
Passardiere & Dufresne, 1992) or text-based (Zhao,
O'Shea & Fung, 1993)
annotation is better in many ways. Unfortunately,
these require advanced
display facilities, while we were limited to IBM
PCs. However, our
preliminary experimental data shows that even
colour-based adaptation can
improve student performance: to reduce the
time and the number of visited
nodes while keeping the same level of
results. These results show that
adaptive navigation gives the user enough
information about related nodes
to avoid unnecessary visits to them.

Thus ISIS-Tutor provides a
hypermedia-like way of investigating the
teaching material. Navigating to
related concepts and examples,
the students can repeat learned pieces of
knowledge as well as learn new
material. While the student works in
hyperspace, the hypermedia component
uses the the stdent model for adaptive
navigation support. On the other
hand, according to the stundent
model-centred architecture, the
student-driven navigation is tracked by the
hypermedia component and
results are reflected in SM by increasing level of
browsed concepts.

The environment is a tool for exploratory
learning of the language
and the acquisition of procedural knowledge.
Regular CDS/ISIS/M facilities
for exploratory learning are quite weak. The
user can see only one of
three main things on the screen: either the print
format, a database
record to be printed, or the results of printing. Also
an ISIS user
usually gets formatted text as the result of the application
of the whole
format to all database records and can hardly understand the
contribution
of any operator to the overall result. It is not so bad for
regular work
but it is very bad for a novice user exploring the language.
It's very
hard to learn from experience in ISIS, because the semantics of
each print
command can't be understood well.

(Figure 6a)

(Figure 6b)

Figure 6. The ISIS-Tutor exploratory environment. The
environment is used
for problem solving support as well as for a free
exploration of the language to be taught.
Parts (a) and (b) show different
steps in processing and visualizing a print format example statement.
On
each part: Top line - the example statement (a processed portion is colored
red); Central part - a database record to be printed;
Bottom lines - the
result of formatting.

The ISIS-Tutor environment was
specially designed to support exploratory
learning. Working with the
learning environment the student can see at the
same time a record of the
sample database, a print format string, and the
result of formatting the
given record by the given format. The user can
change record (select other
database record or create a new one) or change
format (select another
example from a list suggested in the concept
presentation window, edit or
enter his own format). Another important
feature of the environment is the
ability to see the execution result of
each operator in the format
separately. In ISIS-Tutor environment a
student can execute the format
step-by-step or command-by-command thus
learning what changes the executed
command adds to the output (Figure 6).

Summary of the knowledge-based approach

Here
we summarize main features of our approach to integrating a hypermedia
component into an intelligent learning environment:

An ILE is
designed as set of functional modules integrated by linked domain and
student models. All the modules can use the student model for the purpose
of adaptation and update it reflecting the student's work with it.

The
tutoring component applies special knowledge-based technology (Brusilovsky
1992a) for system-controlled sequencing of multi-concept teaching
operations of several kinds.

The central part of the hypermedia
network is designed as the visualized and externalized domain model
network.

All teaching operations are directly represented in the
hypermedia network and interlinked with all related concepts.

An
external representation of a concept or a teaching operation is generated
or assembled from its internal frame-based representation.

The content
of the hypermedia page is adapted to the student knowledge reflected in the
student model.

The hyper-links from general index and from any node to
related nodes are marked visually reflecting the current educational state
and goal state of the related nodes for the given student.

Student
interaction with the hypermedia component is reflected in the student model
and can be used by other components of ILE.

We will be working
further testing and improving our approach. In
designing a new system by
our approach, a major part of our work is just
repeating and re-coding the
same things that we did designing older
systems. We are now going further
and starting the design of an authoring system based on our approach.
The following two section discuss in more details two special
features of
ISIS-Tutor - the student model-centred architecture and the
adaptive
navigation support.

Student model
centered architecture for the integration of system
components

In our
work on student model centered ILE we were going from the ITS side,
thus we
adopted traditional ITS architecture as a basis for the
architecture of
student model centered ILE. The traditional ITS
architecture includes three
main components: the expertise component, the
tutoring component, and the
student modeling component. Each of the
components localizes one of the
three kinds of knowledge important for
intelligent tutoring: knowledge
about domain, knowledge about tutoring,
and knowledge about student and
student modeling (Wenger, 1987). According
to this architecture, the
student model represents the student's
understanding of material to be
taught. The student model is used by the
tutoring component to provide
adaptive tutoring on various levels. The
results of student's work with
teaching operations are returned back to
student modeling (diagnostic)
component and used to update the student
model. This is called the student
modelling loop.

To use ITS experience in student modelling we decide
to apply the regular
student model being used by the tutoring component of
an ILE as the
central student-user model for overall ILE. In our first
systems the
tutoring component provides regular student modelling loop,
while other
components of the ILE just use this central student model for
adaptation.
The only problem was to choose the kind of student model which
can be used
by all the components. We use overlay model, which contains one
integer
counter for each subject knowledge element measuring
student's
understanding of this element. This kind of overlay model is
powerful and
general enough to be used by different components of ILE. The
student
model is kept updated by the special evaluation module which
analyzes the
results of student' problem solving activity.

The above
overlay model is accessible for all the modules of ILE and can
be used by
each of them to adapt its behavior to the student knowledge.
However to
avoid using senseless numbers and to provide more flexibility
we suggested
a threshold technique. Each of the ILE components can
distinguish
several distinct knowledge states for each knowledge elements.
Each of
these states has special meaning for the module from the
adaptation point
of view. The more states a module can take into account
the more complex
adaptation it can provide. Simple modules can distinguish
only two states -
for example unknown and known, while the
most adaptive
tutoring module of ITEM/IP can distinguish six states
(Brusilovsky 1992a).
To map a particular integer value of the overlay
model into a set of states
each module use integer thresholds which divide
the possible range of
values of the counter into required number of
intervals corresponding to
knowledge states recognizable by the module.
Thus simple modules use one
threshold only, while the tutoring module uses
five thresholds. Each module
use own set of thresholds over the central
student model. These thresholds
can be different for different
knowledge elements and different students.
The threshold technique
provides a good flexibility, giving the way to
adapt the student modelling
mechanism to the knowledge elements of
different difficultly and to
different classes of students.

We
applied the above student model centered architecture in several ILE
for
different domains. These ILE have the same overall architecture, but
use
different sets of modules and demonstrates several possible ways
of
applying the overlay student model for adaptation. For example,
ITEM/IP
contains the following adaptive modules: the strategy module
which
supports adaptive sequencing of teaching operations, the
visual
interpreter which uses the student's current knowledge level to
provide
adaptive error handling and adaptive visualization, and the
presentation
module which generates an adaptive description of a concept or
a construct
when introducing or repeating it. All these modules refer to
the same six
knowledge states (five thresholds) for each domain knowledge
element in
its adaptation rules. ISIS-Tutor adds
the new adaptive module -
the hypermedia component. As we already
mentioned, it distinguish four
knowledge states for each concept:
not-ready-to-be-learned,
ready-to-be-learned, in-work, and learned.

The methods of adaptation
used in the our ILE are rather simple. The goal
was not to improve the
known methods of adaptation of various components,
but to build a system
where most of the modules can use the same student
model to adapt their
performance, in various ways, to the knowledge of the
given user. On the
further steps some simple methods of adaptation can be
replaced by more
sophisticated technologies developed in the fields of
intelligent
interfaces and intelligent help systems.

Adaptive navigation support as a kind of adaptive
hypermedia

Here we put adaptive navigation support in more general
context of
adaptation in hypermedia. What can be adapted in adaptive
hyperemedia (AH)
are the content of a hypermedia page and the links from a
page (including
index pages and maps) to related pages.
We distinguish
these two techniques of adaptation and call the first
technique adaptive
presentation (or content-level adaptation) and the
second technique
adaptive navigation support (or link-level adaptation).
Adaptive
presentation is the most popular and the most studied way of
hypermedia
adaptation (Boecker, Hohl & Schwab, 1990; de Rosis, De Carolis
& Pizzutilo,
1993; Boyle & Encarnacion, 1994).

With adaptive presentation the
content of a hypermedia page is generated
or assembled from pieces
according to the user's class and knowledge
state. Generally, qualified
users receive more detailed and deep
information, while novices receive
more additional explanation. By
adaptive navigation support we mean
all the ways to play with visible
links which can support hyperspace
navigation. Previous works (Boecker,
Hohl & Schwab, 1990; Kaplan, Fenwick &
Chen, 1993) suggest adaptive
ordering technique for adaptive navigation
support. This technique apply
user model and some user-valuable criteria to
adapt the order of
presentation for all possible links. It gives the user a
hint which link
to follow (the more close to the top, the more relevant the
link is). We
think that adaptive ordering technique provides a good way to
support user
navigation in the pages with dozens of possible links, but has
less sense
in educational context when the number of links is smaller. Some
research
also shows that the stable order of options in menus is important
for
novices. We apply another technique for adaptive navigation support
in
educational hypermedia: visual adaptive annotation of links
(augmenting
links with dynamic comments in any form) according user's goal
and
knowledge state (Brusilovsky, Pesin & Zyryanov, 1993; Brusilovsky &
Pesin,
1994).

We expect that adaptive annotation can give the users
some
additional information about accessible nodes and thus reduce
their
floundering in the hyperspace. In particular, we hope that it reduce
the
number of "orientation" visits when the user visits related nodes just
for
several seconds to see what is around him. At present there are very
few
studies which investigate the effectiveness of AH. The
experiments,
reported in (Boyle & Encarnacion, 1994) shows that adaptive
presentation
increase user performance. The work (Kaplan, Fenwick & Chen,
1993) reports
positive experimental results of adaptive ordering technique.
By now there
was no experiments with adaptive annotation technique, though
some related
research shows that even non- adaptive annotation, which tells
the user
more about the nodes designated by annotated links, can increase
students
performance (Zhao, O'Shea & Fung, 1993). It was the goal of our
recent
experiment with ISIS-Tutor to check the effectiveness of
adaptive
annotation in educational context.

As we described above,
hypermedia component of ISIS-Tutor uses colours and
special marks to
annotate the set of links leading from the current node
to related nodes
(and from index page to all nodes) according to the
current user knowledge
and educational goals. In the version which was
used in experiment the
links to concepts which are the goal of the current
lesson are marked with
a sign "-", links to not-ready-to-be-learned
concepts were not specially
coloured, ready-to-be-learned were coloured
red, both in-work and learned
were coloured green, and learned concepts
was additionally marked with sign
"+". Links to problems were adaptively
annotated by the same way.

Twenty six subjects
(first year computer science students of the Moscow
State University) took
part in the experiment. They were briefly
introduced to ISIS-Tutor and then
had up to 45 minutes to work with the
system. The same educational goal
(ten concepts and ten test problems) was
set to all the students. To finish
the course, each user had to solve all
ten problems. The subjects were
divided randomly into three groups. Group
A worked with hypermedia without
any adaptation, the students however were
given the numbers of goal
concepts (in index and all menus the name of the
concept is always preceded
by its number). Group B worked with adaptive
hypermedia as described above.
Group C worked with restrictive version of
the same adaptive hypermedia:
the links to all not-ready-to-be-learned
concepts and problems and to all
concepts and problems outside the
learning goals were excluded from index
and all other menus (Figure 7). The idea of
this
restriction is to reduce the cognitive load of the student by
excluding
"not useful" information. All actions of the students working
with the
system are recorded and then analyzed to compare various aspects
of user
performance.

Group

Overall numberof steps

Repetitionsof studied concepts

Transitionsconcept -> concept

Transitionsindex -> concept

A

78

17

8.3

22.14

B

53

6.75

1.4

15.5

C

61.3

11

2.7

16.3

The results of the
experiment are shown in the table above (all data are
average numbers for
each group). As we can see, the overall number of
navigation steps, the
number of repetitions of previously studied
concepts, the number of
transitions from concept to concept and from index
to concept are seriously
less for AH. Moreover, this difference is usually
bigger for
non-restrictive hypermedia. The results of our experiment shows
that
adaptive visual annotation of hypermedia links in educational context
can
really reduce user's floundering in the hyperspace and make the
learning
with hypermedia more goal-oriented. With adaptive annotation the
user can
achieve the same result with less navigation steps and by less
visits to
hypernodes. It is interesting to compare our results with the
results
presented in (Boyle & Encarnacion, 1994).

This work reports that
adaptive presentation in hypermedia can reduce the
time for learning the
material and improve the comprehension of it, but
can not reduce the number
of nodes visited in the process of learning. In
the same time, adaptive
annotation of links can hardly improve the quality
of learning, but can
reduce the number of visited nodes thus further
reducing the learning time.
These techniques looks complimentary and can
be used together for further
improvement of the effectiveness of learning
with hypermedia.

We plan
further experiments to investigate various aspects of adaptive
educational
hypermedia.